Overview

Dataset statistics

Number of variables10
Number of observations50000
Missing cells2978
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 MiB
Average record size in memory80.0 B

Variable types

Numeric8
Categorical2

Alerts

carrier is highly overall correlated with orgHigh correlation
duration is highly overall correlated with mileHigh correlation
mile is highly overall correlated with durationHigh correlation
org is highly overall correlated with carrierHigh correlation
delay has 2978 (6.0%) missing valuesMissing
mon has 4704 (9.4%) zerosZeros
dow has 7072 (14.1%) zerosZeros
delay has 713 (1.4%) zerosZeros

Reproduction

Analysis started2024-04-07 12:33:52.123603
Analysis finished2024-04-07 12:34:03.475557
Duration11.35 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

mon
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2351
Minimum0
Maximum11
Zeros4704
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2024-04-07T14:34:03.540013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4377586
Coefficient of variation (CV)0.65667487
Kurtosis-1.1480689
Mean5.2351
Median Absolute Deviation (MAD)3
Skewness0.11107439
Sum261755
Variance11.818184
MonotonicityNot monotonic
2024-04-07T14:34:03.632359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 4779
9.6%
0 4704
9.4%
6 4612
9.2%
1 4573
9.1%
2 4419
8.8%
7 4327
8.7%
11 4302
8.6%
3 4126
8.3%
4 4066
8.1%
8 3512
7.0%
Other values (2) 6580
13.2%
ValueCountFrequency (%)
0 4704
9.4%
1 4573
9.1%
2 4419
8.8%
3 4126
8.3%
4 4066
8.1%
5 4779
9.6%
6 4612
9.2%
7 4327
8.7%
8 3512
7.0%
9 3372
6.7%
ValueCountFrequency (%)
11 4302
8.6%
10 3208
6.4%
9 3372
6.7%
8 3512
7.0%
7 4327
8.7%
6 4612
9.2%
5 4779
9.6%
4 4066
8.1%
3 4126
8.3%
2 4419
8.8%

dom
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.66196
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2024-04-07T14:34:03.718269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7724881
Coefficient of variation (CV)0.56011432
Kurtosis-1.1882301
Mean15.66196
Median Absolute Deviation (MAD)8
Skewness0.011073272
Sum783098
Variance76.956548
MonotonicityNot monotonic
2024-04-07T14:34:03.803924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
11 1763
 
3.5%
22 1763
 
3.5%
21 1736
 
3.5%
1 1729
 
3.5%
7 1722
 
3.4%
4 1704
 
3.4%
8 1691
 
3.4%
12 1668
 
3.3%
19 1661
 
3.3%
6 1659
 
3.3%
Other values (21) 32904
65.8%
ValueCountFrequency (%)
1 1729
3.5%
2 1620
3.2%
3 1571
3.1%
4 1704
3.4%
5 1541
3.1%
6 1659
3.3%
7 1722
3.4%
8 1691
3.4%
9 1610
3.2%
10 1623
3.2%
ValueCountFrequency (%)
31 949
1.9%
30 1403
2.8%
29 1513
3.0%
28 1624
3.2%
27 1620
3.2%
26 1598
3.2%
25 1583
3.2%
24 1652
3.3%
23 1654
3.3%
22 1763
3.5%

dow
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.95236
Minimum0
Maximum6
Zeros7072
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2024-04-07T14:34:03.897858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9660335
Coefficient of variation (CV)0.6659193
Kurtosis-1.2398224
Mean2.95236
Median Absolute Deviation (MAD)2
Skewness0.0046486105
Sum147618
Variance3.8652877
MonotonicityNot monotonic
2024-04-07T14:34:03.973594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 7900
15.8%
1 7441
14.9%
2 7286
14.6%
4 7265
14.5%
0 7072
14.1%
3 7057
14.1%
6 5979
12.0%
ValueCountFrequency (%)
0 7072
14.1%
1 7441
14.9%
2 7286
14.6%
3 7057
14.1%
4 7265
14.5%
5 7900
15.8%
6 5979
12.0%
ValueCountFrequency (%)
6 5979
12.0%
5 7900
15.8%
4 7265
14.5%
3 7057
14.1%
2 7286
14.6%
1 7441
14.9%
0 7072
14.1%

carrier
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
UA
13170 
AA
11316 
OO
8148 
WN
5333 
B6
5267 
Other values (4)
6766 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters100000
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUA
3rd rowUA
4th rowAA
5th rowAA

Common Values

ValueCountFrequency (%)
UA 13170
26.3%
AA 11316
22.6%
OO 8148
16.3%
WN 5333
10.7%
B6 5267
 
10.5%
OH 3229
 
6.5%
US 2740
 
5.5%
HA 707
 
1.4%
AQ 90
 
0.2%

Length

2024-04-07T14:34:04.065907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T14:34:04.155071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ua 13170
26.3%
aa 11316
22.6%
oo 8148
16.3%
wn 5333
10.7%
b6 5267
 
10.5%
oh 3229
 
6.5%
us 2740
 
5.5%
ha 707
 
1.4%
aq 90
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 36599
36.6%
O 19525
19.5%
U 15910
15.9%
W 5333
 
5.3%
N 5333
 
5.3%
B 5267
 
5.3%
6 5267
 
5.3%
H 3936
 
3.9%
S 2740
 
2.7%
Q 90
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 94733
94.7%
Decimal Number 5267
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 36599
38.6%
O 19525
20.6%
U 15910
16.8%
W 5333
 
5.6%
N 5333
 
5.6%
B 5267
 
5.6%
H 3936
 
4.2%
S 2740
 
2.9%
Q 90
 
0.1%
Decimal Number
ValueCountFrequency (%)
6 5267
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 94733
94.7%
Common 5267
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 36599
38.6%
O 19525
20.6%
U 15910
16.8%
W 5333
 
5.6%
N 5333
 
5.6%
B 5267
 
5.6%
H 3936
 
4.2%
S 2740
 
2.9%
Q 90
 
0.1%
Common
ValueCountFrequency (%)
6 5267
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 36599
36.6%
O 19525
19.5%
U 15910
15.9%
W 5333
 
5.3%
N 5333
 
5.3%
B 5267
 
5.3%
6 5267
 
5.3%
H 3936
 
3.9%
S 2740
 
2.7%
Q 90
 
0.1%

flight
Real number (ℝ)

Distinct3297
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2054.3134
Minimum1
Maximum6941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2024-04-07T14:34:04.268424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile52
Q1413
median1075
Q32720.5
95-th percentile6350
Maximum6941
Range6940
Interquartile range (IQR)2307.5

Descriptive statistics

Standard deviation2182.4715
Coefficient of variation (CV)1.0623849
Kurtosis-0.5132689
Mean2054.3134
Median Absolute Deviation (MAD)800
Skewness1.040294
Sum1.0271567 × 108
Variance4763182
MonotonicityNot monotonic
2024-04-07T14:34:04.373034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 107
 
0.2%
133 103
 
0.2%
177 103
 
0.2%
19 101
 
0.2%
11 99
 
0.2%
345 99
 
0.2%
1 97
 
0.2%
321 96
 
0.2%
117 95
 
0.2%
52 91
 
0.2%
Other values (3287) 49009
98.0%
ValueCountFrequency (%)
1 97
0.2%
2 49
0.1%
3 74
0.1%
4 61
0.1%
5 36
 
0.1%
6 83
0.2%
7 5
 
< 0.1%
8 55
0.1%
9 33
 
0.1%
10 88
0.2%
ValueCountFrequency (%)
6941 7
< 0.1%
6939 4
< 0.1%
6937 9
< 0.1%
6936 1
 
< 0.1%
6919 3
 
< 0.1%
6913 2
 
< 0.1%
6912 2
 
< 0.1%
6898 2
 
< 0.1%
6897 1
 
< 0.1%
6892 4
< 0.1%

org
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
ORD
19337 
SFO
9557 
JFK
7958 
LGA
4995 
SJC
3057 
Other values (3)
5096 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters150000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJFK
2nd rowORD
3rd rowSFO
4th rowORD
5th rowORD

Common Values

ValueCountFrequency (%)
ORD 19337
38.7%
SFO 9557
19.1%
JFK 7958
15.9%
LGA 4995
 
10.0%
SJC 3057
 
6.1%
SMF 3032
 
6.1%
TUS 1055
 
2.1%
OGG 1009
 
2.0%

Length

2024-04-07T14:34:04.478625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T14:34:04.584000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ord 19337
38.7%
sfo 9557
19.1%
jfk 7958
15.9%
lga 4995
 
10.0%
sjc 3057
 
6.1%
smf 3032
 
6.1%
tus 1055
 
2.1%
ogg 1009
 
2.0%

Most occurring characters

ValueCountFrequency (%)
O 29903
19.9%
F 20547
13.7%
R 19337
12.9%
D 19337
12.9%
S 16701
11.1%
J 11015
 
7.3%
K 7958
 
5.3%
G 7013
 
4.7%
L 4995
 
3.3%
A 4995
 
3.3%
Other values (4) 8199
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 150000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 29903
19.9%
F 20547
13.7%
R 19337
12.9%
D 19337
12.9%
S 16701
11.1%
J 11015
 
7.3%
K 7958
 
5.3%
G 7013
 
4.7%
L 4995
 
3.3%
A 4995
 
3.3%
Other values (4) 8199
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 150000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 29903
19.9%
F 20547
13.7%
R 19337
12.9%
D 19337
12.9%
S 16701
11.1%
J 11015
 
7.3%
K 7958
 
5.3%
G 7013
 
4.7%
L 4995
 
3.3%
A 4995
 
3.3%
Other values (4) 8199
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 29903
19.9%
F 20547
13.7%
R 19337
12.9%
D 19337
12.9%
S 16701
11.1%
J 11015
 
7.3%
K 7958
 
5.3%
G 7013
 
4.7%
L 4995
 
3.3%
A 4995
 
3.3%
Other values (4) 8199
 
5.5%

mile
Real number (ℝ)

HIGH CORRELATION 

Distinct323
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean882.40112
Minimum67
Maximum4243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2024-04-07T14:34:04.720984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile158
Q1342
median651
Q31182
95-th percentile2475
Maximum4243
Range4176
Interquartile range (IQR)840

Descriptive statistics

Standard deviation701.23279
Coefficient of variation (CV)0.7946871
Kurtosis0.93209254
Mean882.40112
Median Absolute Deviation (MAD)335
Skewness1.2434593
Sum44120056
Variance491727.42
MonotonicityNot monotonic
2024-04-07T14:34:04.821326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
733 2062
 
4.1%
1846 1062
 
2.1%
2586 993
 
2.0%
337 983
 
2.0%
802 746
 
1.5%
740 659
 
1.3%
1745 627
 
1.3%
867 587
 
1.2%
334 586
 
1.2%
612 585
 
1.2%
Other values (313) 41110
82.2%
ValueCountFrequency (%)
67 98
 
0.2%
77 208
 
0.4%
78 150
 
0.3%
84 130
 
0.3%
86 390
0.8%
94 41
 
0.1%
100 538
1.1%
106 56
 
0.1%
109 112
 
0.2%
110 7
 
< 0.1%
ValueCountFrequency (%)
4243 52
 
0.1%
4184 18
 
< 0.1%
3711 25
 
0.1%
3303 9
 
< 0.1%
2846 26
 
0.1%
2845 58
 
0.1%
2704 234
 
0.5%
2689 18
 
< 0.1%
2640 38
 
0.1%
2586 993
2.0%

depart
Real number (ℝ)

Distinct1020
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.130953
Minimum0.25
Maximum23.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2024-04-07T14:34:04.932994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile6.75
Q110
median14.08
Q318.08
95-th percentile21.37
Maximum23.98
Range23.73
Interquartile range (IQR)8.08

Descriptive statistics

Standard deviation4.6940523
Coefficient of variation (CV)0.3321823
Kurtosis-1.1019593
Mean14.130953
Median Absolute Deviation (MAD)4.01
Skewness-0.0063818206
Sum706547.63
Variance22.034127
MonotonicityNot monotonic
2024-04-07T14:34:05.047354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 747
 
1.5%
13.17 463
 
0.9%
8 459
 
0.9%
12 430
 
0.9%
9 418
 
0.8%
7 411
 
0.8%
19 394
 
0.8%
11 387
 
0.8%
20 383
 
0.8%
18 319
 
0.6%
Other values (1010) 45589
91.2%
ValueCountFrequency (%)
0.25 14
< 0.1%
0.42 1
 
< 0.1%
0.67 12
< 0.1%
0.75 1
 
< 0.1%
0.83 8
 
< 0.1%
1 4
 
< 0.1%
4 1
 
< 0.1%
5 22
< 0.1%
5.17 2
 
< 0.1%
5.58 2
 
< 0.1%
ValueCountFrequency (%)
23.98 43
0.1%
23.97 1
 
< 0.1%
23.93 2
 
< 0.1%
23.92 21
< 0.1%
23.83 5
 
< 0.1%
23.75 8
 
< 0.1%
23.73 1
 
< 0.1%
23.67 18
< 0.1%
23.65 2
 
< 0.1%
23.58 8
 
< 0.1%

duration
Real number (ℝ)

HIGH CORRELATION 

Distinct398
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.76582
Minimum30
Maximum560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2024-04-07T14:34:05.165736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile57
Q185
median125
Q3193
95-th percentile336
Maximum560
Range530
Interquartile range (IQR)108

Descriptive statistics

Standard deviation87.045073
Coefficient of variation (CV)0.5735486
Kurtosis0.74398912
Mean151.76582
Median Absolute Deviation (MAD)45
Skewness1.1495317
Sum7588291
Variance7576.8447
MonotonicityNot monotonic
2024-04-07T14:34:05.292199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 1634
 
3.3%
80 1358
 
2.7%
75 1358
 
2.7%
70 1012
 
2.0%
90 976
 
2.0%
110 751
 
1.5%
135 639
 
1.3%
155 617
 
1.2%
150 601
 
1.2%
120 579
 
1.2%
Other values (388) 40475
81.0%
ValueCountFrequency (%)
30 6
 
< 0.1%
31 43
 
0.1%
34 538
1.1%
36 21
 
< 0.1%
37 7
 
< 0.1%
38 10
 
< 0.1%
39 16
 
< 0.1%
40 25
 
0.1%
41 37
 
0.1%
42 122
 
0.2%
ValueCountFrequency (%)
560 10
< 0.1%
556 6
< 0.1%
552 3
 
< 0.1%
549 6
< 0.1%
548 6
< 0.1%
547 2
 
< 0.1%
541 5
< 0.1%
540 9
< 0.1%
536 2
 
< 0.1%
535 1
 
< 0.1%

delay
Real number (ℝ)

MISSING  ZEROS 

Distinct466
Distinct (%)1.0%
Missing2978
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean28.663796
Minimum-72
Maximum1370
Zeros713
Zeros (%)1.4%
Negative16332
Negative (%)32.7%
Memory size390.8 KiB
2024-04-07T14:34:05.409353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-72
5-th percentile-21
Q1-6
median16
Q344
95-th percentile132
Maximum1370
Range1442
Interquartile range (IQR)50

Descriptive statistics

Standard deviation54.240343
Coefficient of variation (CV)1.8922945
Kurtosis25.877822
Mean28.663796
Median Absolute Deviation (MAD)23
Skewness3.1272522
Sum1347829
Variance2942.0148
MonotonicityNot monotonic
2024-04-07T14:34:05.540721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7 920
 
1.8%
-6 901
 
1.8%
-8 897
 
1.8%
-4 872
 
1.7%
-5 829
 
1.7%
-2 828
 
1.7%
-9 825
 
1.7%
-10 822
 
1.6%
-3 808
 
1.6%
-1 784
 
1.6%
Other values (456) 38536
77.1%
(Missing) 2978
 
6.0%
ValueCountFrequency (%)
-72 1
 
< 0.1%
-70 2
< 0.1%
-66 2
< 0.1%
-62 1
 
< 0.1%
-61 1
 
< 0.1%
-60 1
 
< 0.1%
-58 3
< 0.1%
-57 4
< 0.1%
-56 2
< 0.1%
-55 3
< 0.1%
ValueCountFrequency (%)
1370 1
< 0.1%
1034 1
< 0.1%
1008 1
< 0.1%
965 1
< 0.1%
867 1
< 0.1%
826 1
< 0.1%
819 1
< 0.1%
636 1
< 0.1%
529 1
< 0.1%
511 1
< 0.1%

Interactions

2024-04-07T14:34:02.246864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:56.631842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:57.657430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:58.491404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:59.223275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:59.976045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:00.755763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:01.478477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:02.337688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:56.837693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:57.771624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:58.579046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:59.311877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:00.100239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:00.846434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:01.589216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:02.437039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:56.954682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:57.885268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:58.677855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:59.425927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:00.187925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:00.926517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:01.693649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:02.543567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:57.059507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:57.989808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:58.767395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:59.538342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:00.318863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:01.017491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:01.787088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:02.644355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:57.169336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:58.097943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:58.867555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:59.628846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:00.404049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:01.110529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:01.896758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:02.740780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:57.276566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:58.207475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:58.958276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:59.710141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:00.505717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:01.190700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:01.985878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:02.825050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:57.388448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:58.301595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:59.041747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:59.787918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:00.590046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:01.275675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:02.071154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:02.919480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:57.507208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:58.393158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:59.128472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:33:59.882391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:00.671677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:01.361212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-07T14:34:02.156971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-04-07T14:34:05.648357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
carrierdelaydepartdomdowdurationflightmilemonorg
carrier1.000-0.0800.0130.000-0.010-0.287-0.036-0.2150.0120.512
delay-0.0801.0000.194-0.002-0.0100.0590.0200.052-0.1080.045
depart0.0130.1941.0000.004-0.030-0.0630.015-0.079-0.0100.092
dom0.000-0.0020.0041.0000.0010.002-0.010-0.0010.0150.012
dow-0.010-0.010-0.0300.0011.0000.012-0.0080.012-0.0110.023
duration-0.2870.059-0.0630.0020.0121.000-0.4090.968-0.0090.273
flight-0.0360.0200.015-0.010-0.008-0.4091.000-0.4310.0180.207
mile-0.2150.052-0.079-0.0010.0120.968-0.4311.000-0.0190.232
mon0.012-0.108-0.0100.015-0.011-0.0090.018-0.0191.0000.027
org0.5120.0450.0920.0120.0230.2730.2070.2320.0271.000

Missing values

2024-04-07T14:34:03.192792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-07T14:34:03.357155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

mondomdowcarrierflightorgmiledepartdurationdelay
011206US19JFK21539.48351NaN
10222UA1107ORD31616.338230.0
22204UA226SFO3376.1782-8.0
39131AA419ORD123610.33195-5.0
4425AA325ORD2588.9265NaN
5521UA704SFO5507.981022.0
6726AA380ORD73310.8313554.0
71166UA1477ORD14408.00232-7.0
81225UA620SJC18297.98250-13.0
91181OO5590SFO1587.776088.0
mondomdowcarrierflightorgmiledepartdurationdelay
499908125OO5572SFO32911.259298.0
499911016AA1609ORD111818.92185-25.0
499924246AA2329ORD80213.50145-5.0
499936255HA305OGG10011.5834NaN
499941240OO6356SFO59920.4310427.0
49995983OO6529ORD10913.085540.0
499961273AA194SFO270414.4232524.0
4999710215UA520ORD7838.23124-13.0
499983200UA835LGA7338.0015025.0
499991170UA766ORD62221.0010319.0